! git log -1 --format="%H"

497b097917797016a5c8d397282faf8cdc866d11

import sys
# Install gurobi
!{sys.executable} -m pip install -i https://pypi.gurobi.com gurobipy
!{sys.executable} -m pip install plotly geopy

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd
from itertools import product

import gurobipy as gp
from gurobipy import GRB, quicksum

# These are ours
import problem
import visualization

Looking in indexes: https://pypi.gurobi.com
Requirement already satisfied: gurobipy in /opt/anaconda3/lib/python3.8/site-packages (9.1.1)
Requirement already satisfied: plotly in /opt/anaconda3/lib/python3.8/site-packages (4.14.3)
Requirement already satisfied: geopy in /opt/anaconda3/lib/python3.8/site-packages (2.1.0)
Requirement already satisfied: retrying>=1.3.3 in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.3.3)
Requirement already satisfied: six in /opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.15.0)
Requirement already satisfied: geographiclib<2,>=1.49 in /opt/anaconda3/lib/python3.8/site-packages (from geopy) (1.50)

def normalize(i, j):
    assert i != j
    return (i, j) if i < j else (j, i)

Problem configuration

from model import Autonomax, Config

# Run on the first |C| cities (mostly for testing)
C = 41

# What scenario we will be utilizing
scenario = 0
# The number of cities in the core net
NC = 4
# 1 if the core net should be a cycle; 0 if it shoul be a path.
Z = 1

autonomax = Autonomax(
    Config(
        cities=problem.cities[:C],
        distances=problem.D[:C, :C],
        demand=problem.B[scenario][:C],
        core_city_count=NC,
        core_net_is_cycle=Z,
    )
)

Academic license - for non-commercial use only - expires 2021-05-15
Using license file /Users/sjurwold/gurobi.lic

idx = next(v for v,city in enumerate(problem.cities) if city=="Östersund")
ostersund_cc = autonomax.model.addConstr(
    (autonomax.is_control_center[idx] == 1)
)

A = autonomax.model.getA()

count = lambda d: len(d) if isinstance(d, gp.tupledict) else 1

V = sum(count(v) for v in autonomax.variables.values())
C = sum(count(c) for c in autonomax.constraints.values())

print(f'V = {V}, C = {C}')

fig, ax = plt.subplots(1, figsize=(128, 128 * C/V))
ax.set_xticklabels([])
ax.set_yticklabels([])
plt.spy(A)


total = 0
print(f'\nCONSTRAINT SETS:')
for (name, constraint) in autonomax.constraints.items():
    constraints_in_set = count(constraint)
    print(f'{constraints_in_set:>5} | {name}')
    #plt.gcf().text(0.07, 0.69 - 0.4 * (total + 0.5 * count(constraint)) / C, name, fontsize=14)
    total += constraints_in_set
    plt.plot([0, V], [total - 0.5, total - 0.5], color='grey')
    
total = 0
print(f'\nVARIABLE SETS:')
for (name, variables) in autonomax.variables.items():
    variables_in_set = count(variables)
    print(f'{variables_in_set:>5} | {name}')
    #plt.gcf().text(0.1 + 0.8 * (total + 0.5*count(variables)) / V, 0.8, name, fontsize=14)
    total += count(variables)
    plt.plot([total - 0.5, total - 0.5], [0, C], color='grey')
    
plt.savefig('constraint-matrix.png', dpi=200)

V = 9389, C = 6153

CONSTRAINT SETS:
    1 | one_control_center
   41 | control_city_directly_connected
 1681 | reduce_control_center_symmetry
   41 | core_city_ub
    1 | cycle_or_path
   41 | disallow_core_tree
    1 | exactly_nc_core_cities
   41 | control_center_is_connected
 2460 | is_connectable
  123 | is_connected_timestep
   41 | connected_graph
   41 | conservation_of_flow
  820 | force_edge_if_flow
  820 | edge_cost_lb

VARIABLE SETS:
   41 | is_control_center
  820 | is_core_edge
   41 | is_core_city
  164 | is_connected_step
 5043 | is_connectable_step
  820 | flow
  820 | abs_flow
  820 | is_sub_edge
  820 | edge_cost

autonomax.model.Params.timeLimit = 600.0
autonomax.model.optimize()

Changed value of parameter timeLimit to 600.0
   Prev: inf  Min: 0.0  Max: inf  Default: inf
Gurobi Optimizer version 9.1.1 build v9.1.1rc0 (mac64)
Thread count: 8 physical cores, 8 logical processors, using up to 8 threads
Optimize a model with 6154 rows, 9389 columns and 30300 nonzeros
Model fingerprint: 0xbafe4d90
Model has 4 SOS constraints
Model has 820 general constraints
Variable types: 2460 continuous, 6929 integer (6929 binary)
Coefficient statistics:
  Matrix range     [1e+00, 2e+05]
  Objective range  [1e+00, 2e+04]
  Bounds range     [1e+00, 1e+02]
  RHS range        [7e-01, 4e+01]
Presolve removed 952 rows and 3495 columns
Presolve time: 0.06s
Presolved: 5202 rows, 5894 columns, 24976 nonzeros
Variable types: 2460 continuous, 3434 integer (3434 binary)
Found heuristic solution: objective 92147.268277
Found heuristic solution: objective 76955.725974

Root relaxation: objective 7.412998e+03, 7951 iterations, 0.19 seconds

    Nodes    |    Current Node    |     Objective Bounds      |     Work
 Expl Unexpl |  Obj  Depth IntInf | Incumbent    BestBd   Gap | It/Node Time

     0     0 7412.99762    0  230 76955.7260 7412.99762  90.4%     -    0s
H    0     0                    75149.913121 7412.99762  90.1%     -    0s
H    0     0                    38444.424737 7412.99762  80.7%     -    0s
     0     0 8645.23655    0  336 38444.4247 8645.23655  77.5%     -    0s
     0     0 8646.58859    0  309 38444.4247 8646.58859  77.5%     -    0s
     0     0 8646.58859    0  307 38444.4247 8646.58859  77.5%     -    0s
     0     0 8907.29676    0  401 38444.4247 8907.29676  76.8%     -    0s
     0     0 8907.29676    0  393 38444.4247 8907.29676  76.8%     -    0s
     0     0 8907.29676    0  384 38444.4247 8907.29676  76.8%     -    0s
H    0     0                    30568.787814 8907.29676  70.9%     -    1s
     0     0 8907.29676    0  423 30568.7878 8907.29676  70.9%     -    1s
     0     0 8907.29676    0  377 30568.7878 8907.29676  70.9%     -    1s
     0     0 8907.29676    0  366 30568.7878 8907.29676  70.9%     -    1s
     0     0 8907.29676    0  361 30568.7878 8907.29676  70.9%     -    1s
     0     0 8907.29676    0  356 30568.7878 8907.29676  70.9%     -    1s
     0     0 8907.29676    0  368 30568.7878 8907.29676  70.9%     -    1s
     0     0 8907.29676    0  375 30568.7878 8907.29676  70.9%     -    1s
     0     0 8907.29676    0  363 30568.7878 8907.29676  70.9%     -    1s
     0     0 8907.29676    0  374 30568.7878 8907.29676  70.9%     -    1s
     0     0 8907.29676    0  361 30568.7878 8907.29676  70.9%     -    1s
     0     0 8907.29676    0  373 30568.7878 8907.29676  70.9%     -    1s
     0     0 8907.29676    0  373 30568.7878 8907.29676  70.9%     -    1s
H    0     0                    23074.151017 8907.29676  61.4%     -    1s
     0     2 8907.29676    0  351 23074.1510 8907.29676  61.4%     -    2s
H   73    70                    22807.419343 9016.27513  60.5%   237    3s
H   73    70                    22542.574249 9016.27513  60.0%   237    3s
H   74    70                    22187.648246 9016.27513  59.4%   234    3s
H  114   105                    21808.381228 9016.27513  58.7%   189    3s
H  114   105                    21414.537481 9016.27513  57.9%   189    3s
H  115   105                    21389.412153 9016.27513  57.8%   187    3s
H  116   105                    21389.330867 9016.27513  57.8%   186    3s
H  117   105                    21225.493366 9016.27513  57.5%   185    3s
H  118   105                    20970.709536 9016.27513  57.0%   183    3s
H  291   259                    20822.843234 9016.27513  56.7%   126    3s
H  326   284                    20752.299978 9016.27513  56.6%   128    4s
H  327   284                    20719.697372 9016.27513  56.5%   128    4s
H  602   496                    20689.252922 9016.27513  56.4%   112    4s
H  605   495                    20652.547989 9016.27513  56.3%   112    4s
H  609   495                    20646.921786 9016.27513  56.3%   112    4s
   685   587 13680.5277  136  214 20646.9218 9016.27513  56.3%   112    5s
H  808   686                    20643.411690 9016.27513  56.3%   106    5s
H  841   690                    20622.967241 9016.27513  56.3%   106    5s
H  844   687                    20612.967241 9016.27513  56.3%   106    5s
H 1162   874                    20586.484021 9016.27513  56.2%   116    6s
  1518  1104 18133.4801  151  350 20586.4840 9016.27513  56.2%   105   10s
H 1521  1050                    20585.252764 9016.27513  56.2%   105   11s
  1525  1056 9953.48209   12  319 20585.2528 9101.58334  55.8%   130   16s
  1562  1077 10358.5144   17  340 20585.2528 9675.72780  53.0%   135   20s
H 1594  1034                    20435.252764 10004.4978  51.0%   139   20s
  1822  1057 18570.9916   36  132 20435.2528 10309.1646  49.6%   156   25s
  2387  1103 16942.6273   28  245 20435.2528 10649.4410  47.9%   176   30s
  3013  1029     cutoff   28      20435.2528 13148.6477  35.7%   198   35s
  3890   941 infeasible   58      20435.2528 15185.5254  25.7%   200   40s
H 3996   885                    20400.910366 15246.2004  25.3%   200   40s
  4959   824 19569.4261   30  219 20400.9104 16880.5468  17.3%   205   45s
  5857   730     cutoff   39      20400.9104 17881.6248  12.3%   211   50s
  7267   590     cutoff   36      20400.9104 19230.7008  5.74%   202   55s

Cutting planes:
  Gomory: 36
  Cover: 126
  Implied bound: 1
  MIR: 73
  Flow cover: 130
  GUB cover: 1
  Inf proof: 3
  Zero half: 16
  RLT: 183
  Relax-and-lift: 2

Explored 17209 nodes (1581340 simplex iterations) in 58.52 seconds
Thread count was 8 (of 8 available processors)

Solution count 10: 20400.9 20435.3 20585.3 ... 20689.3

Optimal solution found (tolerance 1.00e-04)
Best objective 2.040091036565e+04, best bound 2.040091036565e+04, gap 0.0000%

Solution

city_info = pd.DataFrame(autonomax.city_info())
city_info

	Index	Name	IsCoreCity	IsControlCenter	Demand	IngoingFlow	OutgoingFlow
0	0	Boden	False	False	4.5	4.000000e+00	8.5
1	1	Borås	False	False	0.7	1.030000e+01	11.0
2	2	Eskilstuna	False	False	1.1	2.520000e+01	26.3
3	3	Falun	False	False	2.3	0.000000e+00	2.3
4	4	Gävle	False	False	1.0	1.900000e+00	2.9
5	5	Göteborg	False	False	5.8	0.000000e+00	5.8
6	6	Halmstad	False	False	3.4	6.100000e+00	9.5
7	7	Haparanda	False	False	4.6	3.078426e-12	4.6
8	8	Helsingborg	False	False	2.2	3.900000e+00	6.1
9	9	Hudiksvall	False	False	3.9	6.969536e-12	3.9
10	10	Jönköping	False	False	1.2	1.237055e-11	1.2
11	11	Kalmar	False	False	4.0	1.600000e+00	5.6
12	12	Karlskrona	False	False	1.6	0.000000e+00	1.6
13	13	Karlstad	False	False	0.9	0.000000e+00	0.9
14	14	Kiruna	False	False	4.0	0.000000e+00	4.0
15	15	Kristianstad	False	False	3.0	0.000000e+00	3.0
16	16	Lidköping	False	False	1.2	1.340000e+01	14.6
17	17	Linköping	False	False	4.1	7.400000e+00	11.5
18	18	Luleå	False	False	2.0	1.310000e+01	15.1
19	19	Malmö	False	False	2.6	1.300000e+00	3.9
20	20	Motala	False	False	2.4	1.200000e+00	3.6
21	21	Norrköping	False	False	1.7	1.890000e+01	20.6
22	22	Nyköping	False	False	4.6	0.000000e+00	4.6
23	23	Sandviken	True	False	4.5	5.200000e+00	9.7
24	24	Skellefteå	False	False	4.4	1.510000e+01	19.5
25	25	Skövde	False	False	2.5	1.100000e+01	13.5
26	26	Stockholm	False	False	7.0	4.228617e-12	7.0
27	27	Sundsvall	True	False	0.7	1.108000e+02	111.5
28	28	Trelleborg	False	False	1.3	3.907319e-12	1.3
29	29	Uddevalla	False	False	4.0	5.800000e+00	9.8
30	30	Umeå	False	False	3.2	1.950000e+01	22.7
31	31	Uppsala	False	False	1.9	0.000000e+00	1.9
32	32	Varberg	False	False	0.8	9.500000e+00	10.3
33	33	Vetlanda	False	False	2.1	5.300000e+00	7.4
34	34	Vänersborg	False	False	3.6	9.800000e+00	13.4
35	35	Västervik	False	False	1.8	5.600000e+00	7.4
36	36	Västerås	True	False	1.4	7.970000e+01	81.1
37	37	Växjö	False	False	2.3	3.000000e+00	5.3
38	38	Örebro	False	False	4.1	3.260000e+01	36.7
39	39	Örnsköldsvik	False	False	3.1	2.270000e+01	25.8
40	40	Östersund	True	True	0.9	1.115000e+02	0.0

edge_info = pd.DataFrame(autonomax.edge_info())
edge_info

	From	To	Type	Flow	Cost	Distance
0	Skellefteå	Umeå	SUB	19.5	731.139160	111
1	Eskilstuna	Västerås	SUB	26.3	244.508415	43
2	Skövde	Örebro	SUB	13.5	657.432752	132
3	Lidköping	Örebro	SUB	14.6	841.276358	148
4	Stockholm	Västerås	SUB	7.0	234.688115	101
5	Linköping	Norrköping	SUB	11.5	116.139455	44
6	Motala	Örebro	SUB	3.6	110.457934	92
7	Boden	Kiruna	SUB	-4.0	637.913000	291
8	Sandviken	Östersund	CORE	0.0	3130.000000	313
9	Linköping	Vetlanda	SUB	-7.4	389.358963	138
10	Sundsvall	Östersund	CORE	111.5	1660.000000	166
11	Kalmar	Västervik	SUB	5.6	300.208566	139
12	Halmstad	Helsingborg	SUB	-6.1	145.447337	79
13	Umeå	Örnsköldsvik	SUB	22.7	849.479945	111
14	Karlstad	Örebro	SUB	0.9	29.229970	77
15	Halmstad	Varberg	SUB	9.5	167.432227	65
16	Lidköping	Vänersborg	SUB	-13.4	206.938975	60
17	Uddevalla	Vänersborg	SUB	9.8	39.823253	21
18	Gävle	Sandviken	SUB	2.9	21.463257	25
19	Hudiksvall	Sundsvall	SUB	3.9	99.906716	81
20	Sandviken	Västerås	CORE	9.7	1100.000000	110
21	Sundsvall	Örnsköldsvik	SUB	-25.8	1177.683887	127
22	Jönköping	Motala	SUB	1.2	52.590906	108
23	Sundsvall	Västerås	CORE	-81.1	3080.000000	308
24	Eskilstuna	Nyköping	SUB	-4.6	119.995513	83
25	Vetlanda	Växjö	SUB	-5.3	112.394025	72
26	Borås	Varberg	SUB	-10.3	260.758783	84
27	Haparanda	Luleå	SUB	4.6	210.858567	124
28	Luleå	Skellefteå	SUB	15.1	742.410242	133
29	Kristianstad	Växjö	SUB	3.0	134.707658	120
30	Norrköping	Västervik	SUB	-7.4	287.369532	112
31	Falun	Sandviken	SUB	2.3	48.115171	65
32	Gävle	Uppsala	SUB	-1.9	37.924183	60
33	Kalmar	Karlskrona	SUB	-1.6	45.527170	79
34	Helsingborg	Malmö	SUB	-3.9	67.318060	60
35	Göteborg	Uddevalla	SUB	5.8	117.417503	70
36	Malmö	Trelleborg	SUB	-1.3	18.150077	34
37	Västerås	Örebro	SUB	-36.7	1050.855609	93
38	Boden	Luleå	SUB	8.5	58.656839	32
39	Eskilstuna	Norrköping	SUB	-20.6	681.069465	102
40	Borås	Skövde	SUB	11.0	384.262775	105

core_cost = sum(edge_info[edge_info['Type'] == 'CORE']['Cost'])
subnet_cost = sum(edge_info[edge_info['Type'] == 'SUB']['Cost'])
total_cost = core_cost + subnet_cost

print(f'core   = {core_cost:>9.3f}')
print(f'subnet = {subnet_cost:>9.3f}')
print('------------------')
print(f'total  = {total_cost:>9.3f}')
assert abs(autonomax.model.getObjective().getValue() - total_cost) < 1e-6

core   =  8970.000
subnet = 11430.910
------------------
total  = 20400.910

visualization.show(pd.DataFrame(autonomax.city_info()), pd.DataFrame(autonomax.edge_info()))